E. Vonk, L. C. Jain, R. P. Johnson's Automatic Generation of Neural Network Architecture Using PDF

By E. Vonk, L. C. Jain, R. P. Johnson

ISBN-10: 9810231067

ISBN-13: 9789810231064

This publication describes the appliance of evolutionary computation within the automated new release of a neural community structure. The structure has an important impact at the functionality of the neural community. it's the traditional perform to take advantage of trial and blunder to discover an appropriate neural community structure for a given challenge. the method of trial and mistake isn't just time-consuming yet would possibly not generate an optimum community. using evolutionary computation is a step in the direction of automation in neural community structure new release. an outline of the sector of evolutionary computation is gifted, including the organic heritage from which the sphere was once encouraged. the main general methods to a mathematical beginning of the sector of genetic algorithms are given, in addition to an summary of the hybridization among evolutionary computation and neural networks. Experiments at the implementation of automated neural community iteration utilizing genetic programming and one utilizing genetic algorithms are defined, and the efficacy of genetic algorithms as a studying set of rules for a feedforward neural community can also be investigated.

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Extra info for Automatic Generation of Neural Network Architecture Using Evolutionary Computation

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Extended forms of elitism are also possible where the best m chromosomes of the population are retained. Simple elitism is the case where m=\. In effect elitism means that the number of offspring that are generated each generation is reduced from N to N-m replacing the worst N-m individuals in the population. A Steady State Genetic Algorithm with ranked unconditional replacement (Genitor-type) can be seen as a GA using extended elitism with m=N-\. 8 Extensions of the Standard Genetic Algorithm Various extensions have been made to overcome some of the shortcomings of the standard genetic algorithm [59].

One of the hidden neurons performs the OR function, the other the NAND, but it does not matter which hidden neuron performs which. In our example both chromosomes (1 1 0, 0 0 1, 1 1 0) and (0 0 1, 1 1 0, 1 1 0 ) perform the XOR function correctly. The first one by means of AND(OR,NAND) and the second one by AND(NAND,OR). However, since the functioning of their hidden neurons is swapped with respect to one another, standard crossover is not expected to yield a useful offspring. This is because the standard crossover (and mutation) operator does not use any topological information available in the phenotype.

Most mutations are deleterious for the organism, in the case of a lethal gene even resulting in death. The small percentage of mutations that are beneficial provide an increased fitness to the organism and its influence can spread throughout the population. 4 Natural Evolution Some comments regarding theories on natural evolution are presented in this section. The most influential theory on evolution was proposed by Charles Darwin in the last century, and his main ideas form the basis of most present-day evolutionary theories.

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Automatic Generation of Neural Network Architecture Using Evolutionary Computation by E. Vonk, L. C. Jain, R. P. Johnson


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